AIC analyses
Average AIC by age group

Average AIC

AIC difference from best model

Age-related change in parameter estimates from models
Run regressions between model parameters and age
##
## Call:
## lm(formula = LL ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -146.117 -38.809 1.221 37.467 140.946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -252.460 26.180 -9.643 1.58e-15 ***
## age 3.244 1.407 2.305 0.0234 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 63.92 on 90 degrees of freedom
## Multiple R-squared: 0.05576, Adjusted R-squared: 0.04527
## F-statistic: 5.315 on 1 and 90 DF, p-value: 0.02344
##
## Call:
## lm(formula = alphaPosChoice ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2998 -0.1843 -0.1090 0.1042 0.8076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075584 0.115998 0.652 0.516
## age 0.009314 0.006235 1.494 0.139
##
## Residual standard error: 0.2832 on 90 degrees of freedom
## Multiple R-squared: 0.0242, Adjusted R-squared: 0.01335
## F-statistic: 2.232 on 1 and 90 DF, p-value: 0.1387
##
## Call:
## lm(formula = alphaNegChoice ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12509 -0.11233 -0.09502 -0.03547 0.85745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.150066 0.095837 1.566 0.121
## age -0.002127 0.005151 -0.413 0.681
##
## Residual standard error: 0.234 on 90 degrees of freedom
## Multiple R-squared: 0.001892, Adjusted R-squared: -0.009199
## F-statistic: 0.1706 on 1 and 90 DF, p-value: 0.6806
##
## Call:
## lm(formula = alphaPosComp ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15755 -0.13896 -0.11970 -0.00461 0.84894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.113350 0.100912 1.123 0.264
## age 0.001771 0.005424 0.327 0.745
##
## Residual standard error: 0.2464 on 90 degrees of freedom
## Multiple R-squared: 0.001183, Adjusted R-squared: -0.009914
## F-statistic: 0.1066 on 1 and 90 DF, p-value: 0.7448
##
## Call:
## lm(formula = alphaNegComp ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18332 -0.18145 -0.15366 0.05692 0.80513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.816e-01 1.175e-01 1.545 0.126
## age 8.019e-05 6.318e-03 0.013 0.990
##
## Residual standard error: 0.287 on 90 degrees of freedom
## Multiple R-squared: 1.79e-06, Adjusted R-squared: -0.01111
## F-statistic: 0.0001611 on 1 and 90 DF, p-value: 0.9899
##
## Call:
## lm(formula = betaAgency ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.9275 -3.8227 -0.5232 2.4939 18.7135
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0872 2.2647 2.246 0.0271 *
## age 0.2358 0.1217 1.937 0.0558 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.53 on 90 degrees of freedom
## Multiple R-squared: 0.04003, Adjusted R-squared: 0.02937
## F-statistic: 3.753 on 1 and 90 DF, p-value: 0.05584
##
## Call:
## lm(formula = betaMachine ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.3482 -3.1200 -0.6171 2.0051 16.4548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.79744 2.05294 2.824 0.00584 **
## age 0.09143 0.11035 0.829 0.40955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.013 on 90 degrees of freedom
## Multiple R-squared: 0.00757, Adjusted R-squared: -0.003457
## F-statistic: 0.6865 on 1 and 90 DF, p-value: 0.4096
##
## Call:
## lm(formula = agencyBonus ~ age, data = model_params)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.82471 -0.15345 -0.04041 0.04863 1.74151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.132596 0.171398 0.774 0.441
## age 0.010238 0.009213 1.111 0.269
##
## Residual standard error: 0.4185 on 90 degrees of freedom
## Multiple R-squared: 0.01354, Adjusted R-squared: 0.002575
## F-statistic: 1.235 on 1 and 90 DF, p-value: 0.2694
Plot relations between model parameters and age

Parameter summary statistics
Mixed-effects beta analysis
## Mixed Model Anova Table (Type 3 tests, S-method)
##
## Model: estimate ~ ageZ * betaType + (1 | subID)
## Data: betas
## Effect df F p.value
## 1 ageZ 1, 90.00 2.73 .102
## 2 betaType 1, 90.00 10.76 ** .001
## 3 ageZ:betaType 1, 90.00 1.41 .238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ ageZ * betaType + (1 | subID)
## Data: data
##
## REML criterion at convergence: 1109.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3424 -0.4713 -0.1507 0.4003 3.2096
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 12.61 3.551
## Residual 15.24 3.904
## Number of obs: 184, groups: subID, 92
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.3862 0.4689 90.0000 17.883 < 2e-16 ***
## ageZ 0.7771 0.4702 90.0000 1.653 0.10191
## betaType1 0.9439 0.2878 90.0000 3.280 0.00148 **
## ageZ:betaType1 0.3429 0.2886 90.0000 1.188 0.23790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ageZ btTyp1
## ageZ 0.000
## betaType1 0.000 0.000
## ageZ:btTyp1 0.000 0.000 0.000
|
Predictor
|
Estimates
|
SE
|
Statistic
|
p
|
|
intercept
|
8.39
|
0.47
|
17.88
|
<0.001
|
|
age
|
0.78
|
0.47
|
1.65
|
0.100
|
|
decision stage
|
0.94
|
0.29
|
3.28
|
0.001
|
|
age x decision stage
|
0.34
|
0.29
|
1.19
|
0.236
|
Beta plot


Mixed-effects learning rate analysis
## Mixed Model Anova Table (Type 3 tests, S-method)
##
## Model: estimate ~ ageZ * valence * agency + (1 | subID)
## Data: learning_rates
## Effect df F p.value
## 1 ageZ 1, 90.00 0.52 .473
## 2 valence 1, 270.00 3.07 + .081
## 3 agency 1, 270.00 0.25 .618
## 4 ageZ:valence 1, 270.00 1.36 .245
## 5 ageZ:agency 1, 270.00 0.22 .637
## 6 valence:agency 1, 270.00 10.03 ** .002
## 7 ageZ:valence:agency 1, 270.00 0.75 .388
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ ageZ * valence * agency + (1 | subID)
## Data: data
##
## REML criterion at convergence: 107.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3019 -0.5803 -0.3735 0.0936 3.2313
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.003877 0.06226
## Residual 0.065636 0.25619
## Number of obs: 368, groups: subID, 92
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.170801 0.014849 90.000000 11.502 < 2e-16 ***
## ageZ 0.010716 0.014869 90.000000 0.721 0.47299
## valence1 -0.023387 0.013355 269.999999 -1.751 0.08105 .
## agency1 0.006673 0.013355 269.999999 0.500 0.61771
## ageZ:valence1 -0.015570 0.013373 269.999999 -1.164 0.24534
## ageZ:agency1 0.006325 0.013373 269.999999 0.473 0.63660
## valence1:agency1 -0.042297 0.013355 269.999999 -3.167 0.00172 **
## ageZ:valence1:agency1 -0.011560 0.013373 269.999999 -0.864 0.38812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ageZ valnc1 agncy1 agZ:v1 agZ:g1 vln1:1
## ageZ 0.000
## valence1 0.000 0.000
## agency1 0.000 0.000 0.000
## ageZ:valnc1 0.000 0.000 0.000 0.000
## ageZ:agncy1 0.000 0.000 0.000 0.000 0.000
## vlnc1:gncy1 0.000 0.000 0.000 0.000 0.000 0.000
## agZ:vlnc1:1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
##
## Paired t-test
##
## data: model_params$alphaPosChoice and model_params$alphaNegChoice
## t = 3.2464, df = 91, p-value = 0.001636
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 0.05098873 0.21174803
## sample estimates:
## mean difference
## 0.1313684
##
## Paired t-test
##
## data: model_params$alphaPosComp and model_params$alphaNegComp
## t = -0.8713, df = 91, p-value = 0.3859
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.12404217 0.04840164
## sample estimates:
## mean difference
## -0.03782026
|
Predictor
|
Estimates
|
SE
|
Statistic
|
p
|
|
intercept
|
0.17
|
0.01
|
11.50
|
<0.001
|
|
age
|
0.01
|
0.01
|
0.72
|
0.472
|
|
valence
|
-0.02
|
0.01
|
-1.75
|
0.081
|
|
agency
|
0.01
|
0.01
|
0.50
|
0.618
|
|
age x valence
|
-0.02
|
0.01
|
-1.16
|
0.245
|
|
age x agency
|
0.01
|
0.01
|
0.47
|
0.637
|
|
valence x agency
|
-0.04
|
0.01
|
-3.17
|
0.002
|
|
age x valence x agency
|
-0.01
|
0.01
|
-0.86
|
0.388
|
Learning rate plot

Relation between parameter estimates and ‘model-free’
regressions
##
## Call:
## lm(formula = `(Intercept)` ~ agencyBonus, data = voc_REs_RL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8973 -0.5048 -0.0711 0.4379 3.2340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7368 0.1199 -6.142 2.17e-08 ***
## agencyBonus 2.2749 0.2291 9.928 4.03e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9159 on 90 degrees of freedom
## Multiple R-squared: 0.5227, Adjusted R-squared: 0.5174
## F-statistic: 98.57 on 1 and 90 DF, p-value: 4.026e-16
##
## Call:
## lm(formula = zVoC ~ betaAgency, data = voc_REs_RL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86135 -0.30842 -0.04316 0.23221 1.17101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.833300 0.090307 -9.227 1.16e-14 ***
## betaAgency 0.086421 0.008306 10.405 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4447 on 90 degrees of freedom
## Multiple R-squared: 0.546, Adjusted R-squared: 0.541
## F-statistic: 108.3 on 1 and 90 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = zVoC ~ betaAgency + age, data = voc_REs_RL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85984 -0.32425 -0.02079 0.25295 1.14000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.979619 0.187387 -5.228 1.12e-06 ***
## betaAgency 0.084907 0.008487 10.004 3.14e-16 ***
## age 0.008918 0.010004 0.891 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4452 on 89 degrees of freedom
## Multiple R-squared: 0.5501, Adjusted R-squared: 0.5399
## F-statistic: 54.4 on 2 and 89 DF, p-value: 3.676e-16
##
## Call:
## lm(formula = zVoC ~ betaAgency + betaMachine, data = voc_REs_RL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.92713 -0.32224 -0.06426 0.27199 1.17530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.878039 0.097696 -8.987 3.99e-14 ***
## betaAgency 0.081312 0.009342 8.704 1.54e-13 ***
## betaMachine 0.012416 0.010478 1.185 0.239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4437 on 89 degrees of freedom
## Multiple R-squared: 0.5531, Adjusted R-squared: 0.543
## F-statistic: 55.07 on 2 and 89 DF, p-value: 2.721e-16
Questionnaire relations
DOC
##
## Call:
## lm(formula = DOC ~ zAge, data = DOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.234 -6.388 -0.270 7.449 30.317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 95.527 1.255 76.11 <2e-16 ***
## zAge 2.446 1.274 1.92 0.058 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.97 on 89 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03978, Adjusted R-squared: 0.02899
## F-statistic: 3.687 on 1 and 89 DF, p-value: 0.05804
##
## Call:
## lm(formula = DOC ~ zBetaAgency * zAgencyBonus * zAge, data = DOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.177 -6.694 0.498 6.836 28.152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 96.3552 1.4448 66.689 <2e-16 ***
## zBetaAgency -0.6198 1.5563 -0.398 0.691
## zAgencyBonus 0.7234 3.8298 0.189 0.851
## zAge 3.1628 1.4235 2.222 0.029 *
## zBetaAgency:zAgencyBonus 1.4501 2.9050 0.499 0.619
## zBetaAgency:zAge -1.4899 1.4688 -1.014 0.313
## zAgencyBonus:zAge 1.8497 3.6647 0.505 0.615
## zBetaAgency:zAgencyBonus:zAge 1.7630 2.6769 0.659 0.512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.15 on 83 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07712, Adjusted R-squared: -0.0007151
## F-statistic: 0.9908 on 7 and 83 DF, p-value: 0.4437
LOC
##
## Call:
## lm(formula = LOC ~ zAge, data = LOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.4335 -3.3923 -0.4242 3.4805 10.1914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6288 0.4372 28.886 <2e-16 ***
## zAge 0.2453 0.4392 0.559 0.578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.17 on 89 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.003494, Adjusted R-squared: -0.007703
## F-statistic: 0.3121 on 1 and 89 DF, p-value: 0.5778
##
## Call:
## lm(formula = LOC ~ zBetaAgency * zAgencyBonus * zAge, data = LOC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.7161 -2.9065 -0.3207 2.9217 9.7008
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6246 0.4987 25.315 <2e-16 ***
## zBetaAgency -0.9811 0.5369 -1.827 0.0712 .
## zAgencyBonus -1.0330 1.3031 -0.793 0.4302
## zAge 0.6066 0.4829 1.256 0.2126
## zBetaAgency:zAgencyBonus -0.2208 1.0007 -0.221 0.8259
## zBetaAgency:zAge 0.3645 0.5006 0.728 0.4686
## zAgencyBonus:zAge 0.6135 1.2310 0.498 0.6195
## zBetaAgency:zAgencyBonus:zAge 0.6690 0.9099 0.735 0.4643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.15 on 83 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07987, Adjusted R-squared: 0.002267
## F-statistic: 1.029 on 7 and 83 DF, p-value: 0.4171
BDI
##
## Call:
## lm(formula = zBDI ~ zAge, data = BDI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.94728 -0.78671 -0.01517 0.72806 2.78555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.122e-16 1.042e-01 0.000 1.000
## zAge 3.587e-02 1.048e-01 0.342 0.733
##
## Residual standard error: 0.9993 on 90 degrees of freedom
## Multiple R-squared: 0.001301, Adjusted R-squared: -0.009796
## F-statistic: 0.1172 on 1 and 90 DF, p-value: 0.7329
##
## Call:
## lm(formula = zBDI ~ zBetaAgency * zAgencyBonus * zAge, data = BDI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8539 -0.6755 -0.0233 0.6917 2.5970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.002931 0.120219 0.024 0.981
## zBetaAgency 0.013087 0.129916 0.101 0.920
## zAgencyBonus 0.074662 0.314943 0.237 0.813
## zAge -0.005686 0.116933 -0.049 0.961
## zBetaAgency:zAgencyBonus -0.005344 0.241141 -0.022 0.982
## zBetaAgency:zAge -0.160374 0.121634 -1.318 0.191
## zAgencyBonus:zAge -0.157728 0.299366 -0.527 0.600
## zBetaAgency:zAgencyBonus:zAge -0.166452 0.220803 -0.754 0.453
##
## Residual standard error: 1.016 on 84 degrees of freedom
## Multiple R-squared: 0.03632, Adjusted R-squared: -0.04399
## F-statistic: 0.4523 on 7 and 84 DF, p-value: 0.866
STAI
##
## Call:
## lm(formula = zSTAI_t ~ zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.82245 -0.96538 0.01261 0.83118 2.16747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001085 0.104658 0.010 0.992
## zAge 0.060134 0.106243 0.566 0.573
##
## Residual standard error: 0.9982 on 89 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.003587, Adjusted R-squared: -0.007609
## F-statistic: 0.3204 on 1 and 89 DF, p-value: 0.5728
##
## Call:
## lm(formula = zSTAI_t ~ zBetaAgency * zAgencyBonus * zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8159 -0.9588 0.0664 0.8286 1.7670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.010483 0.122103 0.086 0.932
## zBetaAgency -0.057149 0.131628 -0.434 0.665
## zAgencyBonus -0.068192 0.316929 -0.215 0.830
## zAge 0.089023 0.120037 0.742 0.460
## zBetaAgency:zAgencyBonus 0.017827 0.242557 0.073 0.942
## zBetaAgency:zAge -0.054557 0.124118 -0.440 0.661
## zAgencyBonus:zAge 0.085548 0.301177 0.284 0.777
## zBetaAgency:zAgencyBonus:zAge 0.009472 0.222193 0.043 0.966
##
## Residual standard error: 1.022 on 83 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02576, Adjusted R-squared: -0.05641
## F-statistic: 0.3135 on 7 and 83 DF, p-value: 0.946
##
## Call:
## lm(formula = zSTAI_s ~ zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9203 -0.6732 -0.1498 0.4769 3.1426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.937e-16 1.033e-01 0.000 1.000
## zAge 1.368e-01 1.038e-01 1.318 0.191
##
## Residual standard error: 0.9905 on 90 degrees of freedom
## Multiple R-squared: 0.01894, Adjusted R-squared: 0.008035
## F-statistic: 1.737 on 1 and 90 DF, p-value: 0.1909
##
## Call:
## lm(formula = zSTAI_s ~ zBetaAgency * zAgencyBonus * zAge, data = STAI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5559 -0.6835 -0.1344 0.6615 2.8213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08413 0.11552 0.728 0.4685
## zBetaAgency -0.04869 0.12483 -0.390 0.6975
## zAgencyBonus 0.26794 0.30262 0.885 0.3785
## zAge 0.20466 0.11236 1.822 0.0721 .
## zBetaAgency:zAgencyBonus 0.39754 0.23171 1.716 0.0899 .
## zBetaAgency:zAge -0.09125 0.11688 -0.781 0.4372
## zAgencyBonus:zAge -0.17596 0.28766 -0.612 0.5424
## zBetaAgency:zAgencyBonus:zAge -0.12508 0.21217 -0.590 0.5571
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9764 on 84 degrees of freedom
## Multiple R-squared: 0.1102, Adjusted R-squared: 0.03608
## F-statistic: 1.487 on 7 and 84 DF, p-value: 0.1831
---
title: "VoC Analyses Part 3: Analyze Reinforcement-Learning Results"
date: 3/27/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

```{r load packages, include = F}

# list all packages required for the analysis
list.of.packages <- c("tidyverse", "latex2exp", "afex", "sjPlot")

# check if all packages are installed, if not, install them.
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

# load all packages 
lapply(list.of.packages, library, character.only = TRUE)

# add theme for plotting
voc_theme <- function () {
  theme(
    panel.border = element_rect(fill = "transparent", color="gray75"),
    panel.background  = element_blank(),
    plot.background = element_blank(), 
    legend.background = element_rect(fill="transparent", colour=NA),
    legend.key = element_rect(fill="transparent", colour=NA),
    line = element_blank(),
    axis.ticks = element_line(color="gray75"),
    text=element_text(family="Avenir"),
    axis.text = element_text(size = 12),
    axis.title = element_text(size = 15),
    title = element_text(size = 15),
    strip.background = element_blank(),
    strip.text = element_text(size=12)
  )
}

color8 = "#80dbb2"
color1 = "#00b4d8"
color2 = "#0077b6"
color3 = "#03045e"
color4 = "#84347C"
color5 = "#B40424"
color6 = "#EB6D1E"
color7 = "#f5b68f"

scale_this <- function(x){
  (x - mean(x, na.rm=TRUE)) / sd(x, na.rm=TRUE)
}

```

```{r, load data}
#load data
aics = read_csv("RL_modeling/output/aics_all_16_models_100iter.csv")
bics = read_csv("RL_modeling/output/bics_all_16_models_100iter.csv")
```

```{r pivot data longer}
aics1 <- pivot_longer(aics, 
                cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                names_to = "model",
                values_to = "AIC")

bics1 <- pivot_longer(bics, 
                cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                names_to = "model",
                values_to = "BIC")
```


#  AIC analyses
## Average AIC by age group
```{r plot AIC by age group, fig.width = 8, fig.height = 5, units = "in"}

# Add id and other demographic info
sub_info <- read_csv('data/voc_sub_info.csv') %>%
    mutate(age_group = case_when(age < 13 ~ "Children",
                                 age > 12.99 & age < 18 ~ "Adolescents",
                                 age > 17.99 ~ "Adults"))

sub_info$age_group <- factor(sub_info$age_group, levels = c("Children", "Adolescents", "Adults"))

model_results <- full_join(sub_info, aics1, by = c("subID"))

model_results$model <- factor(model_results$model, 
                              levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta",
                                         "oneAlpha_oneBeta_agencyBonus",
                                         "oneAlpha_twoBeta_agencyBonus",
                                         "twoAlpha_oneBeta_agencyBonus",
                                         "twoAlpha_twoBeta_agencyBonus",
                                         "twoAlphaValenced_oneBeta_agencyBonus",
                                         "twoAlphaValenced_twoBeta_agencyBonus",
                                         "fourAlpha_oneBeta_agencyBonus",
                                         "fourAlpha_twoBeta_agencyBonus"))
model_results <- model_results %>%
    mutate(agencyBonus = case_when(str_detect(model, "agency") ~ "With Agency Bonus",
                                  !str_detect(model, "agency") ~ "No Agency Bonus"),
           shortName = str_remove(model, '_agencyBonus'))

model_results$shortName <- factor(model_results$shortName,
                                  levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta"))
                                 
#summarize
model_summary <- model_results %>%
    group_by(age_group, shortName, agencyBonus) %>%
    summarize(meanAIC = mean(AIC))

# # Plot the results by age group 
AIC_age_plot <- ggplot(model_summary, aes(x = age_group, y = meanAIC, fill = shortName))+
    facet_wrap(~agencyBonus) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    coord_cartesian(ylim = c(350, 600)) +
    ylab("Mean AIC") +
    xlab("") +
    voc_theme() +
    theme(axis.text.x = element_text(angle = 60, hjust = 1))
AIC_age_plot
```

## Average AIC 
```{r aic overall plot, fig.width = 6, fig.height = 4, units = "in"}
model_summary_overall <- model_results %>%
    group_by(model, shortName, agencyBonus) %>%
    summarize(meanAIC = mean(AIC))

AIC_plot <- ggplot(model_summary_overall, aes(x = shortName, y = meanAIC, fill = shortName)) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    facet_wrap(~agencyBonus) +
    coord_cartesian(ylim = c(350, 600)) + 
    ylab("Mean AIC") +
    xlab("Model") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    scale_x_discrete(labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    voc_theme() +
        theme(axis.text.x = element_text(angle = 75, hjust = 1),
              legend.position = "none")
AIC_plot

```

## AIC difference from best model
```{r aic overall difference plot, fig.width = 4, fig.height = 5, units = "in"}
#get minimum AIC
minAIC = min(model_summary_overall$meanAIC)

#subtract from mean AICs
model_difference_summary <- model_summary_overall %>%
    mutate(AIC_difference = meanAIC - minAIC[1]) %>%
    filter(agencyBonus == "With Agency Bonus")

#plot
AIC_difference_plot <- ggplot(model_difference_summary, aes(x = shortName, y = AIC_difference, fill = shortName)) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    facet_wrap(~agencyBonus) +
    ylab("AIC Difference") +
    xlab("") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    scale_x_discrete(labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    voc_theme() +
        theme(axis.text.x = element_text(angle = 60, hjust = 1),
              legend.position = "none")
AIC_difference_plot

```


#  Age-related change in parameter estimates from models
```{r parameter estimates}

# load all parameters from each model
model_params <- read_csv("RL_modeling/output/model_fits_real_data/fourAlpha_twoBeta_agencyBonus.csv",
                         col_names = c("negLL",
                                       "logPost",
                                       "AIC",
                                       "BIC",
                                       "alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"))

#add sub ID and information
subID <- read_csv('RL_modeling/output/subIDs.csv')
model_params <- bind_cols(subID, model_params)
model_params <- full_join(sub_info, model_params, by = c("subID"))
```


# Run regressions between model parameters and age
```{r param age regressions}

model_params$LL <- model_params$negLL * -1

# Log likelihood
summary(lm(LL ~ age, data = model_params))
# significant

# Alpha Pos Choice
summary(lm(alphaPosChoice ~ age, data = model_params))
#not significant

# Alpha Neg Choice
summary(lm(alphaNegChoice ~ age, data = model_params))
#not significant

# Alpha Pos Comp
summary(lm(alphaPosComp ~ age, data = model_params))
#not significant

# Alpha Neg Comp
summary(lm(alphaNegComp ~ age, data = model_params))
#not significant

# Beta Agency
summary(lm(betaAgency ~ age, data = model_params))
#significant

# Beta Bandit
summary(lm(betaMachine ~ age, data = model_params))
#not significant

# agency bonus
summary(lm(agencyBonus ~ age, data = model_params))
#not significant
```

# Plot relations between model parameters and age
```{r age parameter plot, fig.width = 7, fig.height = 4, units = "in"}

params_long <- model_params %>%
    pivot_longer(names_to = "param",
                 values_to = "estimate",
                 cols = c(alphaPosChoice:agencyBonus)) 

params_long$param <- factor(params_long$param, 
                            levels = c("alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"),
                            labels = c(TeX("$\\alpha_{choice_+}$"), 
                                       TeX("$\\alpha_{choice_-}$"), 
                                       TeX("$\\alpha_{comp_+}$"), 
                                       TeX("$\\alpha_{comp_-}$"), 
                                       TeX("$\\beta_{agency}$"), 
                                       TeX("$\\beta_{machine}$"),
                                       "Agency~Bonus"
                                ))

params_plot <- ggplot(params_long, aes(x = age, y = estimate, color = param)) +
    facet_wrap(~param, scale = "free", labeller = label_parsed, nrow = 2) +
    geom_point() +
    geom_smooth(method = "lm", aes(fill = param)) +
    ylab("Parameter Estimate") +
    xlab("Age") +
    voc_theme() +
    theme(legend.position = "none")
params_plot
```

# Parameter summary statistics
```{r parameter summary stats}

param_summary <- params_long %>%
    group_by(param) %>%
    summarize(meanEstimate = mean(estimate),
            seEstimate = sd(estimate)/sqrt(n()))
param_summary

```

# Mixed-effects beta analysis
```{r beta regression}
betas <- model_params %>%
    pivot_longer(cols = c(betaAgency, betaMachine),
                 names_to = "betaType",
                 values_to = "estimate") %>%
    select(subID, age, age_group, betaType, estimate) %>%
    unique() 
                               
betas$ageZ <- scale_this(betas$age)

beta_model <- mixed(estimate ~ ageZ * betaType + (1|subID),
                             data = betas,
                             method = "S")
beta_model
summary(beta_model)
```

```{r beta print model stats}

beta_model.lmer <- mixed(estimate ~ ageZ * betaType + (1|subID),
                             data = betas,
                             method = "S",
                             return = "merMod")

tab_model(beta_model.lmer, 
          pred.labels = c("intercept", "age", "decision stage", "age x decision stage"),
          transform = NULL,
          show.est = T, 
          show.se = T, 
          show.stat = T,
          show.ci = F,
          show.re.var = F,
          show.icc = F,
          show.ngroups = F,
          show.obs = F,
          show.r2 = F,
          string.se = "SE",
          emph.p = F,
          string.pred = "Predictor",
          title = "",
          dv.labels = "")
```


## Beta plot
```{r beta plot}

beta_means <- betas %>%
    group_by(age_group, betaType) %>%
    summarize(meanBeta = mean(estimate),
              seBeta = sd(estimate) / sqrt(n()))

beta_plot <- ggplot(beta_means, aes(x = betaType, y = meanBeta, fill = age_group)) +
    geom_bar(color = 'black', stat = "identity", position = "dodge") + 
    geom_errorbar(color = "black", aes(ymin = meanBeta - seBeta, ymax = meanBeta + seBeta), width = .1,
                  position = position_dodge(width = .9)) +
    scale_fill_manual(values = c(color1, color2, color3), name = "Age Group") +
    ylab("Mean Beta") +
    xlab("Decision Stage") +
    scale_x_discrete(labels = c("Agency Decision", "Machine Decision")) +
    voc_theme()
beta_plot 


beta_plot_continuous <- ggplot(betas, aes(color = betaType, y = estimate, x = age)) +
    geom_point() +
    geom_smooth(method = "lm", aes(fill = betaType, color = betaType)) +
    scale_color_manual(values = c(color1, color2), name = "Beta Parameter", labels = c("Agency Decision", "Machine Decision")) +
    scale_fill_manual(values = c(color1, color2), name = "Beta Parameter", labels = c("Agency Decision", "Machine Decision")) +
    ylab("Beta Estimate") +
    xlab("Age") +
    voc_theme()
beta_plot_continuous
```


# Mixed-effects learning rate analysis
```{r learning rate regression}
learning_rates <- model_params %>%
    pivot_longer(cols = c(alphaPosChoice:alphaNegComp),
                 names_to = "learningRate",
                 values_to = "estimate") %>%
    select(subID, age, age_group, learningRate, estimate) %>%
    unique() %>%
    mutate(valence = case_when(str_detect(learningRate, "Pos") ~ "Positive",
                               str_detect(learningRate, "Neg") ~ "Negative"),
           agency = case_when(str_detect(learningRate, "Choice") ~ "Choice",
                              str_detect(learningRate, "Comp") ~ "Comp"))
                               
learning_rates$ageZ <- scale_this(learning_rates$age)

learning_rate_model <- mixed(estimate ~ ageZ * valence * agency + (1|subID),
                             data = learning_rates,
                             method = "S")
learning_rate_model
summary(learning_rate_model)
# valence x agency interaction
# marginal valence x agency x age interaction

#t test between alpha pos choice and alpha neg choice
t.test(model_params$alphaPosChoice, model_params$alphaNegChoice, paired = T)
#significant

#t test between alpha pos comp and alpha neg comp
t.test(model_params$alphaPosComp, model_params$alphaNegComp, paired = T)
#not significant

```

```{r learning rate print model stats}

learning_rate_model.lmer <- mixed(estimate ~ ageZ * valence * agency + (1|subID),
                             data = learning_rates,
                             method = "S",
                             return = "merMod")

tab_model(learning_rate_model.lmer, 
          pred.labels = c("intercept", "age", "valence", "agency", "age x valence", "age x agency", "valence x agency", "age x valence x agency"),
          transform = NULL,
          show.est = T, 
          show.se = T, 
          show.stat = T,
          show.ci = F,
          show.re.var = F,
          show.icc = F,
          show.ngroups = F,
          show.obs = F,
          show.r2 = F,
          string.se = "SE",
          emph.p = F,
          string.pred = "Predictor",
          title = "",
          dv.labels = "")
```

## Learning rate plot
```{r learning rate plot}

learning_rate_means <- learning_rates %>%
    group_by(agency, valence) %>%
    summarize(meanLR = mean(estimate),
              seLR = sd(estimate) / sqrt(n()))

learning_rate_plot <- ggplot(learning_rate_means, aes(x = agency, y = meanLR, fill = valence)) +
    geom_bar(color = 'black', stat = "identity", position = "dodge") + 
    geom_errorbar(color = "black", aes(ymin = meanLR - seLR, ymax = meanLR + seLR), width = .1,
                  position = position_dodge(width = .9)) +
    scale_fill_manual(values = c(color1, color2), name = "Valence") +
    ylab("Mean Learning Rate") +
    xlab("Agency") +
    scale_x_discrete(labels = c("Participant Choice", "Computer Choice")) +
    voc_theme()
learning_rate_plot 
```



# Relation between parameter estimates and 'model-free' regressions
```{r relations between random effects and model parameters - extract REs}

# Read in data
banditTask <- read_csv('data/processed/bandit_task.csv') 

#combine with participant age
banditTask <- full_join(banditTask, sub_info, by = c("subID"))

#scale voc
banditTask$zVoC <- scale_this(banditTask$voc)
banditTask$zTrialOfCond <- scale_this(banditTask$trialOfCond)
banditTask$zAge <- scale_this(banditTask$age)

# predict agency choice from utility of control, trial, linear age
agency_byVOCTrialAge.mixed = mixed(agency ~ zVoC * zTrialOfCond + (zVoC * zTrialOfCond|subID), 
                        data = banditTask, 
                        family = binomial, 
                        method = "LRT", control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6)),
                        return = "merMod") 

#get random effects
voc_REs <- ranef(agency_byVOCTrialAge.mixed)$subID %>%
    rownames_to_column(var = "subID")

#combine with RL estimates
voc_REs_RL <- full_join(voc_REs, model_params, by = 'subID')

```

```{r run regressions REs and model parameters}
#run regressions

#agency bonus
voc_intercept_agencyBonus.lm <- lm(`(Intercept)` ~ agencyBonus, data = voc_REs_RL)
summary(voc_intercept_agencyBonus.lm)

#beta agency
voc_slope_betaAgency.lm <- lm(zVoC ~ betaAgency, data = voc_REs_RL)
summary(voc_slope_betaAgency.lm)

#beta agency controlling for age
voc_slope_betaAgencyAge.lm <- lm(zVoC ~ betaAgency + age, data = voc_REs_RL)
summary(voc_slope_betaAgencyAge.lm)

#beta agency controlling for beta machine
voc_slope_betaMachine.lm <- lm(zVoC ~ betaAgency + betaMachine, data = voc_REs_RL)
summary(voc_slope_betaMachine.lm)

```







# Questionnaire relations

## DOC
```{r doc}
# load questionnaire data
DOC <- read_csv("data/scored_surveys/DOC_scored.csv", col_names = TRUE) 

# merge with model params
DOC <- left_join(DOC, model_params)

# z score continuous variables
DOC$zAge <- scale_this(DOC$age)
DOC$zBetaAgency <- scale_this(DOC$betaAgency)
DOC$zAgencyBonus <- scale_this(DOC$agencyBonus)

# relation between DOC and age
lm(DOC ~ zAge, DOC) %>% summary()
#marginal positive effect (p = .058)

# relation between DOC and VoC
lm(DOC ~ zBetaAgency * zAgencyBonus *zAge, DOC) %>% summary()
# no effects

```

## LOC
```{r loc}
# load questionnaire data
LOC <- read_csv("data/scored_surveys/LOC_scored.csv", col_names = TRUE) 

# merge with model params
LOC <- left_join(LOC, model_params)

#z score continuous variables
LOC$zAge <- scale_this(DOC$age)
LOC$zBetaAgency <- scale_this(LOC$betaAgency)
LOC$zAgencyBonus <- scale_this(LOC$agencyBonus)

# relation between LOC and age
lm(LOC ~ zAge, LOC) %>% summary()
# no effect

# relation between LOC and VoC
lm(LOC ~ zBetaAgency * zAgencyBonus * zAge, LOC) %>% summary()
# no effects
```


## BDI
```{r bdi}
# load questionnaire data
BDI <- read_csv("data/scored_surveys/BDI_scored.csv", col_names = TRUE) 

# merge with model params
BDI <- left_join(BDI, model_params)

#z score continuous variables
BDI$zAge <- scale_this(BDI$age)
BDI$zBetaAgency <- scale_this(BDI$betaAgency)
BDI$zAgencyBonus <- scale_this(BDI$agencyBonus)

# relation between BDI and age
lm(zBDI ~ zAge, BDI) %>% summary()
# no effect

# relation between BDI and VoC 
lm(zBDI ~ zBetaAgency * zAgencyBonus *zAge, BDI) %>% summary()
# no effects

```


## STAI
```{r stai}
# load questionnaire data
STAI <- read_csv("data/scored_surveys/STAI_scored.csv", col_names = TRUE) 

# merge with model params
STAI <- left_join(STAI, model_params)

#z score continuous variables
STAI$zAge <- scale_this(STAI$age)
STAI$zBetaAgency <- scale_this(STAI$betaAgency)
STAI$zAgencyBonus <- scale_this(STAI$agencyBonus)

# relation between STAI_t and age
lm(zSTAI_t ~ zAge, STAI) %>% summary()
# no effect

# relation between STAI_t and VoC
lm(zSTAI_t  ~ zBetaAgency * zAgencyBonus *zAge, STAI) %>% summary()
# no effect

# relation between STAI_s and age
lm(zSTAI_s ~ zAge, STAI) %>% summary()
# no effects

# relation between STAI_s and VoC
lm(zSTAI_s  ~ zBetaAgency * zAgencyBonus *zAge, STAI) %>% summary()
# no effects
```